Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest

Abstract

Abbreviations IS = imaging spectroscopy; LiDAR = light detection and ranging; N:P ratio = nitrogen to phosphorus ratio; N = nitrogen; P = phosphorus Nomenclature Little (1979) Abstract Questions: Can the ratio of nitrogen to phosphorus (N:P ratio) be predicted at canopy level using imaging spectroscopy (IS) and light detection and ranging (LiDAR) remote sensing data? How do temporal variation and difference in spatial resolution of these data sources affect prediction accuracy of the canopy N:P ratio?Location: Boreal mixedwood forest, northern Ontario, Canada.Methods: Canopy N:P ratio was estimated using spectral indices calculated from IS data at two spatial resolutions, airborne and space-borne, across two summers. The relationship between the canopy N:P ratio and forest structure was investigated through analysis of LiDAR data. The impact of temporal variation on canopy N:P ratio and the different spatial resolution of IS data on prediction accuracy for canopy N:P was addressed. Maps of canopy N:P ratio generated from airborne and space-borne IS data were generated.Results: Airborne and space-borne IS data explained 70% and 69% of the variation in canopy N:P, ratio, with predictions errors of 5.0% and 7.2%, respectively, in two consecutive years. Predictions differed significantly with changes in spatial resolution. Predictive models obtained from LiDAR data explained 54% and 67% of the variation in canopy N:P ratio, with prediction errors of 6.1% and 7.5%, respectively, for the 2 yrs. Conclusions:The results show that canopy N:P ratio can be predicted with remote sensing data based on the relationship between canopy N:P ratio and crown closure at this site. The spatial variation due to the mixed deciduous and coniferous forest type is the underlying mechanism that generates the observed spatial pattern in canopy N:P ratio in this ecosystem, and the canopy N:P ratio map displays this variation.

Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

“…Spatial distribution of canopy macronutrient concentration at GRFS mimics the functional type variation and thus, the spatial patterns of macronutrients are driven by the functional types' distribution at the site. Forest type variation was also found to be the underlying factor in explaining the observed spatial patterns of canopy N:P ratio at this site [54].…”

“…Crown closure reflects the amount of green foliage in the canopy and directly controls the spectral response collected over the canopy. Crown closure was identified as the biophysical variable linking spectral data to canopy N:P ratio over two seasons at the same site [54] and it was also found to be controlling the spectral response in jack pine stands of northern Ontario, Canada [69].…”

“…This is also likely to be the case in the mixedwood boreal forest, which contains deciduous and coniferous species and forest types. The canopy N:P ratio has been predicted and mapped based on the spatial variation generated by deciduous and coniferous forest types in a mixedwood boreal forest of northern Ontario [54].…”

“…If a covariate that establishes a link between the macronutrient concentration and LiDAR data can be identified, then it would be possible to predict macronutrients using LiDAR data. For example, crown closure was identified as the canopy structural metric that made it possible to relate LiDAR data to canopy N:P ratio in the mixedwood boreal forest [54].…”

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

“…Additionally, Townsend et al [52] disagreed that the %N-NIR relationship is necessarily spurious, as Wright et al [14] and Ollinger [53] indicated that the canopy structure and leaf properties may co-vary across plant functional types. Species and plant functional types (i.e., broadleaf and coniferous forest types) account for most of the variance of canopy chemistry that has been demonstrated across tropical [54][55][56][57], temperate [58], boreal forests [29,59], and Mediterranean ecosystems [60]. The link between species and canopy biochemistry can be explained by the concept of 'global leaf economics spectrum' [14], which means that the key plant traits such as leaf mass per area, specific leaf area, leaf nitrogen, leaf phosphorous, leaf lifespan, and photosynthesis fall into a spectrum across plant species, and species converge towards the functional traits globally [14,61].…”